Update coursera links

src/data/roadmaps/ai-data-scientist/ai-data-scientist.json

@@ -316,7 +316,7 @@
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           "y": "583",
           "properties": {
-            "controlName": "ext_link:coursera.org/specializations/mathematics-machine-learning#courses"
+            "controlName": "ext_link:imp.i384100.net/baqMYv"
           },
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@@ -443,7 +443,7 @@
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-            "controlName": "ext_link:coursera.org/learn/algebra-and-differential-calculus-for-data-science#syllabus"
+            "controlName": "ext_link:imp.i384100.net/LX5M7M"
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@@ -596,7 +596,7 @@
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           "properties": {
-            "controlName": "ext_link:coursera.org/learn/stanford-statistics#syllabus"
+            "controlName": "ext_link:imp.i384100.net/3eRv4v"
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@@ -736,7 +736,7 @@
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-            "controlName": "ext_link:coursera.org/learn/statistical-analysis-hypothesis-testing-sas#syllabus"
+            "controlName": "ext_link:imp.i384100.net/vN0JAA"
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@@ -862,7 +862,7 @@
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           "properties": {
-            "controlName": "ext_link:coursera.org/learn/probability-statistics#syllabus"
+            "controlName": "ext_link:imp.i384100.net/daDM6Q"
           },
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@@ -2157,7 +2157,7 @@
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           "y": "1102",
           "properties": {
-            "controlName": "ext_link:coursera.org/learn/erasmus-econometrics#syllabus"
+            "controlName": "ext_link:imp.i384100.net/k0krYL"
           },
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@@ -2529,7 +2529,7 @@
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           "properties": {
-            "controlName": "ext_link:coursera.org/learn/linear-regression-business-statistics#about"
+            "controlName": "ext_link:imp.i384100.net/9g97Ke"
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@@ -3018,7 +3018,7 @@
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           "y": "2037",
           "properties": {
-            "controlName": "ext_link:coursera.org/specializations/algorithms#courses"
+            "controlName": "ext_link:imp.i384100.net/5gqv4n"
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@@ -3375,7 +3375,7 @@
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           "y": "1749",
           "properties": {
-            "controlName": "ext_link:coursera.org/projects/exploratory-data-analysis-python-pandas"
+            "controlName": "ext_link:imp.i384100.net/AWAv4R"
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@@ -3437,7 +3437,7 @@
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           "y": "1804",
           "properties": {
-            "controlName": "ext_link:coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning#syllabus"
+            "controlName": "ext_link:imp.i384100.net/GmQMLE"
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@@ -3499,7 +3499,7 @@
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           "y": "1860",
           "properties": {
-            "controlName": "ext_link:coursera.org/projects/exploratory-data-analysis-seaborn"
+            "controlName": "ext_link:imp.i384100.net/ZQmMgR"
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@@ -3786,7 +3786,7 @@
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           "y": "2185",
           "properties": {
-            "controlName": "ext_link:coursera.org/specializations/machine-learning-introduction#courses"
+            "controlName": "ext_link:imp.i384100.net/oqGkrg"
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@@ -4045,7 +4045,7 @@
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-            "controlName": "ext_link:coursera.org/specializations/deep-learning#courses"
+            "controlName": "ext_link:imp.i384100.net/Wq9MV3"
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@@ -4560,7 +4560,7 @@
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-            "controlName": "ext_link:coursera.org/specializations/machine-learning-engineering-for-production-mlops#courses"
+            "controlName": "ext_link:imp.i384100.net/nLA5mx"
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src/data/roadmaps/ai-data-scientist/content/classic-advanced-ml.md

@@ -1,7 +1,7 @@
 # Classic/Advanced ML
 
 - [Open Machine Learning Course](https://mlcourse.ai/book/topic01/topic01_intro.html)
-- [Coursera: Machine Learning Spcialization](https://www.coursera.org/specializations/machine-learning-introduction#courses)
+- [Coursera: Machine Learning Specialization](https://imp.i384100.net/oqGkrg)
 - [Pattern Recognition and Machine Learning by Christopher Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
 - [Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop](https://github.com/gerdm/prml)
 

src/data/roadmaps/ai-data-scientist/content/data-understanding.md

@@ -1,6 +1,6 @@
 # Data Understanding, Analysis and Visualization
 
-- [Exploratory Data Analysis With Python and Pandas](https://www.coursera.org/projects/exploratory-data-analysis-python-pandas)
-- [Exploratory Data Analysis for Machine Learning](https://www.coursera.org/learn/ibm-exploratory-data-analysis-for-machine-learning#syllabus)
-- [Exploratory Data Analysis with Seaborn](https://www.coursera.org/projects/exploratory-data-analysis-seaborn)
+- [Exploratory Data Analysis With Python and Pandas](https://imp.i384100.net/AWAv4R)
+- [Exploratory Data Analysis for Machine Learning](https://imp.i384100.net/GmQMLE)
+- [Exploratory Data Analysis with Seaborn](https://imp.i384100.net/ZQmMgR)
 

src/data/roadmaps/ai-data-scientist/content/deployment-models.md

@@ -1,4 +1,4 @@
 # MLOps
 
-- [Machine Learning Engineering for Production (MLOps) Specialization](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops#courses)
+- [Machine Learning Engineering for Production (MLOps) Specialization](https://imp.i384100.net/nLA5mx)
 

src/data/roadmaps/ai-data-scientist/content/diff-calculus.md

@@ -1,4 +1,4 @@
 # Differential Calculus
 
-- [Algebra and Differential Calculus for Data Science](https://coursera.org/learn/algebra-and-differential-calculus-for-data-science#syllabus)
+- [Algebra and Differential Calculus for Data Science](https://imp.i384100.net/LX5M7M)
 

src/data/roadmaps/ai-data-scientist/content/fully-connected-nn.md

@@ -3,5 +3,5 @@
 - [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
 - [Attention is All you Need](https://arxiv.org/pdf/1706.03762.pdf)
 - [Deep Learning Book](https://www.deeplearningbook.org/)
-- [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning#courses)
+- [Deep Learning Specialization](https://imp.i384100.net/Wq9MV3)
 

src/data/roadmaps/ai-data-scientist/content/hypothesis-testing.md

@@ -1,4 +1,4 @@
 # Hypothesis Testing
 
-- [Introduction to Statistical Analysis: Hypothesis Testing](https://www.coursera.org/learn/statistical-analysis-hypothesis-testing-sas#syllabus)
+- [Introduction to Statistical Analysis: Hypothesis Testing](https://imp.i384100.net/vN0JAA)
 

src/data/roadmaps/ai-data-scientist/content/learn-dsa.md

@@ -2,4 +2,4 @@
 
 - [Learn Algorithms](https://leetcode.com/explore/learn/)
 - [Leetcode - Study Plans](https://leetcode.com/studyplan/)
-- [Algorithms Specialization](https://coursera.org/specializations/algorithms#courses)
+- [Algorithms Specialization](https://imp.i384100.net/5gqv4n)

src/data/roadmaps/ai-data-scientist/content/linear-algebra-calc-mathana.md

@@ -1,4 +1,4 @@
 # Learn Algebra, Calculus, Mathematical Analysis
 
-- [Mathematics for Machine Learning Specialization](https://www.coursera.org/specializations/mathematics-machine-learning#courses)
+- [Mathematics for Machine Learning Specialization](https://imp.i384100.net/baqMYv)
 

src/data/roadmaps/ai-data-scientist/content/probability-sampling.md

@@ -1,4 +1,4 @@
 # Probability and Sampling
 
-- [Probability and Statistics: To p or not to p?](https://www.coursera.org/learn/probability-statistics#syllabus)
+- [Probability and Statistics: To p or not to p?](https://imp.i384100.net/daDM6Q)
 

src/data/roadmaps/ai-data-scientist/content/regression-time-series-fitting-distr.md

@@ -2,11 +2,11 @@
 
 - [10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/)
 - [Dougherty Intro to Econometrics 4th edition](https://www.academia.edu/33062577/Dougherty_Intro_to_Econometrics_4th_ed_small)
-- [Econometrics: Methods and Applications](https://www.coursera.org/learn/erasmus-econometrics#syllabus)
+- [Econometrics: Methods and Applications](https://imp.i384100.net/k0krYL)
 - [Kaggle - Learn Time Series](https://www.kaggle.com/learn/time-series)
 - [Time series Basics : Exploring traditional TS](https://www.kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts#Hierarchical-time-series)
 - [How to Create an ARIMA Model for Time Series Forecasting in Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python)
 - [11 Classical Time Series Forecasting Methods in Python](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/)
 - [Blockchain.com Data Scientist TakeHome Test](https://github.com/stalkermustang/bcdc_ds_takehome)
-- [Linear Regression for Business Statistics](https://www.coursera.org/learn/linear-regression-business-statistics#about)
+- [Linear Regression for Business Statistics](https://imp.i384100.net/9g97Ke)
 

src/data/roadmaps/ai-data-scientist/content/stats-clt.md

@@ -1,4 +1,4 @@
 # Statistics, CLT
 
-- [Introduction to Statistics](https://coursera.org/learn/stanford-statistics#syllabus)
+- [Introduction to Statistics](https://imp.i384100.net/3eRv4v)